On Di, 2015-02-10 at 15:07 -0700, cjw wrote:
It seems to be agreed that there are weaknesses in the existing Numpy Matrix Class.
Some problems are illustrated below.
Not to delve deeply into a discussion, but unfortunately, there seem far more fundamental problems because of the always 2-D thing and the simple fact that matrix is more of a second class citizen in numpy (or in other words a lot of this is just the general fact that it is an ndarray subclass). I think some of these issues were summarized in the discussion about the @ operator. I am not saying that a matrix class separate from numpy cannot solve these, but within numpy it seems hard.
I'll try to put some suggestions over the coming weeks and would appreciate comments.
Colin W.
Test Script:
if __name__ == '__main__': a= mat([4, 5, 6]) # Good print('a: ', a) b= mat([4, '5', 6]) # Not the expected result print('b: ', b) c= mat([[4, 5, 6], [7, 8]]) # Wrongly accepted as rectangular print('c: ', c) d= mat([[1, 2, 3]]) try: d[0, 1]= 'b' # Correctly flagged, not numeric except ValueError: print("d[0, 1]= 'b' # Correctly flagged, not numeric", ' ValueError') print('d: ', d)
Result:
*** Python 2.7.9 (default, Dec 10 2014, 12:28:03) [MSC v.1500 64 bit (AMD64)] on win32. ***
a: [[4 5 6]] b: [['4' '5' '6']] c: [[[4, 5, 6] [7, 8]]] d[0, 1]= 'b' # Correctly flagged, not numeric ValueError d: [[1 2 3]]
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